Sign up to receive free email alerts when patent applications with chosen keywords are publishedSIGN UP

Abstract:

A method includes receiving during a first time interval associated with
a path of motion of a dynamic body, image data associated with a
plurality of images of the dynamic body. The plurality of images include
an indication of a position of a first marker coupled to a garment at a
first location, and a position of a second marker coupled to the garment
at a second location. The garment is coupled to the dynamic body. During
a second time interval, an image from the plurality of images is
automatically identified that includes a position of the first marker
that is substantially the same as a position of a first localization
element relative to the dynamic body and a position of the second marker
that is substantially the same as a position of the second localization
element relative to the dynamic body.

Claims:

1-18. (canceled)

19. A processor-readable medium storing code representing instructions to
cause a processor to perform a process, the code comprising code to:
receive, during a first time interval, image data associated with a
plurality of 3-D images of a dynamic body during a path of motion of the
dynamic body, the dynamic body having an apparatus coupled to the dynamic
body, the apparatus having a plurality of markers coupled thereto, and
wherein the plurality of markers change in orientation relative to each
other during the first time interval; receive, during a second time
interval after the first time interval, data associated with a plurality
of localization elements coupled to the apparatus, the data including a
position of each of the plurality of localization elements during a path
of motion of the dynamic body, and wherein the plurality of localization
elements change in orientation relative to each other during the second
time interval; automatically compare the data associated with the
position of each of the localization elements from the plurality of
localization elements with a position of each marker from the plurality
of markers identified from the data associated with a plurality of 3-D
images; and automatically identifying a marker from the plurality of
markers within the plurality of 3-D images having substantially the same
position as a localization element from the plurality of localization
elements relative to the dynamic body based on the comparing.

20. The processor-readable medium of claim 19, further comprising code
to: select an image from the plurality of images associated with the
identified marker.

21. The processor-readable medium of claim 19, further comprising code
to: automatically identify a second marker from the plurality of markers
within the plurality of 3-D images having substantially the same position
as a second localization element from the plurality of localization
elements relative to the dynamic body based on the comparing; and select
an image from the plurality of 3-D images associated with the identified
first marker and the identified second marker.

22. The processor-readable medium of claim 19, further comprising code
to: receive during the second time interval data associated with a
position of a third localization element relative to the dynamic body,
the third localization element coupled to the dynamic body; project the
position of the third localization element on to the data associated with
a plurality of images; and determine that a third marker either one of
exists or does not exists in an image from the plurality of images at
substantially the same position as the third localization element.

23. The processor-readable medium of claim 19, wherein the automatically
identifying includes automatically correlating the identified position of
the localization element with a position of the marker within an image
from the plurality of images.

24. The processor-readable medium of claim 19, further comprising code
to: automatically register the path of motion of the dynamic body
associated with the second time interval with the path of motion of the
dynamic body associated with the first time interval, based on the
automatically identifying.

25. A processor-readable medium storing code representing instructions to
cause a processor to perform a process, the code comprising code to:
receive, during a first time interval, image data associated with a
plurality of 3-D images of a dynamic body during a path of motion of the
dynamic body, the dynamic body having an apparatus coupled thereto, the
apparatus having a plurality of markers coupled thereto, wherein the
plurality of markers change in orientation relative to each other during
the first time interval; receive, during a second time interval after the
first time interval, data associated with a plurality of localization
elements coupled to the apparatus, the data including a position of each
of the plurality of localization elements during a path of motion of the
dynamic body, wherein the plurality of localization elements change in
orientation relative to each other during the second time interval;
automatically segment the data associated with a plurality of 3-D images
from the first time interval to identify a position of each of the at
least one markers from the plurality of markers; automatically correlate
the data associated with the position of the at least one markers from
the plurality of markers from the first time interval with the data
associated with the position of each of the localization elements from
the plurality of localization elements from the second time interval; and
automatically register a path of motion of the dynamic body during the
first time interval with a path of motion of the dynamic body during the
second time interval based on the automatically correlating.

26. (canceled)

27. The processor-readable medium of claim 24, wherein the automatic
registration includes identifying at least one temporal reference within
the plurality of images and whether the at least one temporal reference
is associated with at least one of the first marker or the second marker,
wherein the identifying provides a navigational path for a medical
instrument to be directed based on the identified image.

28. The processor-readable medium of claim 27, wherein the navigational
path is used to guide a medical instrument to the dynamic body while
performing a medical procedure on the dynamic body during the second time
interval.

29. The processor-readable medium of claim 19, further comprising code
to: automatically identify a first marker and a second marker from the
plurality of markers within the plurality of 3-D images having
substantially the same position as a second localization element from the
plurality of localization elements relative to the dynamic body based on
the comparing; and identify an image from the plurality of 3-D images
associated with the identified first marker and the identified second
marker.

30. The processor-readable medium of claim 29, wherein the automatic
registration includes identifying at least one temporal reference within
the plurality of images and whether the at least one temporal reference
is associated with at least one of the first marker or the second marker,
wherein the identifying provides a navigational path for a medical
instrument to be directed based on the identified image.

31. The processor-readable medium of claim 30, wherein the navigational
path is used to guide a medical instrument to the dynamic body while
performing a medical procedure on the dynamic body during the second time
interval.

Description:

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application is a continuation-in-part of U.S. patent
application Ser. No. 11/224,028, filed Sep. 13, 2005, entitled "Apparatus
and Method for Image Guided Accuracy Verification," the disclosure of
which is hereby incorporated by reference in its entirety.

BACKGROUND

[0002] The invention relates generally to a medical device and
particularly to an apparatus and method associated with image guided
medical procedures.

[0003] Image guided surgery (IGS), also known as image guided intervention
(IGI), enhances a physician's ability to locate instruments within
anatomy during a medical procedure. IGS can include 2-dimensional (2-D)
and 3-dimensional (3-D) applications.

[0004] Existing imaging modalities can capture the movement of dynamic
anatomy. Such modalities include electrocardiogram (ECG)-gated or
respiratory-gated magnetic resonance imaging (MRI) devices, ECG-gated or
respiratory-gated computer tomography (CT) devices, and cinematography
(CINE) fluoroscopy. The dynamic imaging modalities can capture the
movement of anatomy over a periodic cycle of that movement by sampling
the anatomy at several instants during its characteristic movement and
then creating a set of image frames or volumes. Such images can be used
to help a physician navigate a medical instrument to the desired location
on the anatomical body during a medical procedure performed on the
anatomical body at a later time.

[0005] Typical image-guided medical systems require manual user input to
identify a pre-procedural image that corresponds to the same position and
orientation of an anatomical body during a medical procedure. These
manual operations can lead to greater errors and reduced efficiency in
image-guided procedures.

[0006] Thus, a need exists for a method and apparatus that can
automatically identify pre-procedural images of a targeted anatomical
body that can be used to help a physician navigate a medical instrument
to a selected location on the anatomical body during a range of motion of
the anatomical body.

SUMMARY OF THE INVENTION

[0007] Apparatuses and methods for performing gated instrument navigation
on dynamic anatomy with automatic image registration are disclosed
herein. In one embodiment, a method includes receiving during a first
time interval image data associated with a plurality of images of a
dynamic body. The plurality of images includes an indication of a
position of a first marker on a garment coupled to the dynamic body and a
position of a second marker on the garment coupled to the dynamic body.
The first marker is coupled to the garment at a first location, and the
second marker is coupled to the garment at a second location. The first
time interval is associated with a path of motion of the dynamic body.
During a second time interval after the first time interval, data is
received that is associated with a position of a first localization
element relative to the dynamic body, and data is received that is
associated with a position of a second localization element relative to
the dynamic body. The first localization element is coupled to the
garment at the first location, and the second localization element is
coupled to the garment at the second location. The second time interval
is associated with a path of motion of the dynamic body. During the
second time interval, an image from the plurality of images is
automatically identified that includes a position of the first marker
that is substantially the same as the position of the first localization
element relative to the dynamic body and a position of the second marker
that is substantially the same as the position of the second localization
element relative to the dynamic body.

BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The present invention is described with reference to the
accompanying drawings.

[0009]FIG. 1 is a schematic illustration of various devices used with a
method according to an embodiment of the invention.

[0010]FIG. 2 is a schematic illustration of various devices used with a
method according to an embodiment of the invention.

[0011]FIG. 3 is a front perspective view of an apparatus according to an
embodiment of the invention.

[0012] FIG. 4 is a graphical representation illustrating the function of
an apparatus according to an embodiment of the invention.

[0013] FIG. 5 is a schematic illustration of an example of voxels of a
connected-component in a 3-D volume according to an embodiment of the
invention.

[0014] FIG. 6 is a schematic illustration of an example of voxels of a
connected-component in a 3-D volume according to an alternative
embodiment of the invention.

[0015] FIG. 7 is a flowchart illustrating a method according to an
embodiment of the invention.

[0016] FIG. 8 is a schematic illustration of the flow of information
during an automatic segmentation process.

DETAILED DESCRIPTION

[0017] A method according to an embodiment of the invention includes
capturing images of a dynamic body during a path of motion of the dynamic
body pre-procedurally (also referred to herein as "first time interval").
The images can be used to assist a physician in navigating a medical
instrument to a desired location on the dynamic body during a medical
procedure performed at a later time (also referred to herein as "second
time interval"). The method uses a system configured to automatically
perform segmentation, correlation and registration between data obtained
in "model space" or "image space" (position data taken pre-procedurally)
and data obtained in "physical space" (position data obtained during a
later medical procedure).

[0018] Specifically, an apparatus is configured to be coupled to a
selected dynamic body, such as selected dynamic anatomy of a patient.
Dynamic anatomy can be, for example, any portion of the body associated
with anatomy that moves during its normal function (e.g., the heart,
lungs, kidneys, liver and vasculature). The apparatus can include, for
example, two or more markers configured to be coupled to a patient and
two or more localization elements configured to be coupled to the patient
proximate the markers. In other embodiments, the apparatus can include,
for example, a garment configured to be coupled to a patient, two or more
markers coupled to the garment, and two or more localization elements
coupled to the garment at a location proximate the markers.

[0019] A processor, such as a computer, is configured to receive the
pre-procedural image data associated with the dynamic body taken during a
pre-surgical or pre-procedural first time interval. The image data can
include an indication of a position of each of the markers for multiple
instants in time during the first time interval. The processor can also
receive position data associated with the localization elements during a
second time interval in which a surgical procedure or other medical
procedure is being performed. The processor can use the position data
received from the localization elements and the position data received
from the images to automatically identify an image from the
pre-procedural images where the position of the markers at a given
instant in time during the pre-procedural imaging is substantially the
same as the position of the localization elements corresponding to those
markers, at a given instant of time during the later medical procedure.

[0020] A physician or other healthcare professional can use the images
that were automatically identified by the processor during a medical
procedure performed during the second time interval, such as, for
example, an image-guided medical procedure involving temporal
registration and gated navigation. For example, when a medical procedure
is performed on a targeted anatomy of a patient, such as a heart, the
physician may not be able to utilize an imaging device during the medical
procedure to guide him to the targeted area within the patient. Markers
or fiducials can be positioned or coupled to the patient proximate the
targeted anatomy prior to the medical procedure, and pre-procedural
images can be taken of the targeted area during a first time interval.
The markers or fiducials can be viewed within the image data, which can
include an indication of the position of the markers during a given path
of motion of the targeted anatomy (e.g., the heart) during the first time
interval. Such motion can be due, for example, to inspiration (i.e.,
inhaling) and expiration (i.e., exhaling) of the patient, or due to the
heart beating. During a medical procedure, performed during a second time
interval, such as a procedure on a heart, with the markers coupled to the
patient at the same location/position as during the first time interval,
the processor receives data from the localization elements associated
with a position of the localization elements at a given instant in time
during the medical procedure (or second time interval).

[0021] Because the markers are positioned at the same location on the
patient relative to the dynamic body during both the first time interval
and the second time interval, and the localization elements are coupled
to the patient proximate the location of the markers, a correlation can
be made between the position data in image space and the position data in
physical space. For example, a position of the markers at an instant in
time during the pre-procedural imaging corresponds to a specific position
and orientation of the dynamic body at an instant in time during the path
of motion of the dynamic body as viewed in the image data. When the
medical procedure is performed during the second time interval, a
position of the localization elements likewise corresponds to a specific
positioning of the dynamic body at an instant in time during the path of
motion of the dynamic body. Although the marker-localization element
combinations can move relative to each other, for example, as the dynamic
anatomy moves, the markers are in a fixed position relative to the
patient during both the first time interval and the second time interval.
As stated above, the localization elements are coupled to the patient
proximate the markers, thus, when the position of the localization
elements (identified during the medical procedure) is substantially the
same as the position of the markers (identified in the image space), the
image corresponding to that position of the markers is representative of
the position of the dynamic body for that instant during the medical
procedure.

[0022] An automatic segmentation-correlation-registration process can be
performed after the image dataset is imported into the processor and the
localization elements are connected to the processor. Once performed, the
correlation does not change during the course of the procedure and the
model space marker positions provide a baseline position for the temporal
registration. After the segmentation-correlation and baseline
registration has been computed, the localization element locations are
sampled automatically and continuously to determine when the dynamic body
is at or near the position at which the images were acquired. The affine
rigid-body transformation is computed automatically and continuously
until a temporal gating threshold is exceeded, indicating that the
dynamic body is no longer near the same configuration as where the images
were acquired. The automatic process produces simulated real-time,
intra-procedural images illustrating the orientation and shape of the
targeted anatomy as a catheter or similar structure is navigated to the
targeted anatomy. Thus, during the medical procedure, the physician can
view selected image(s) of the targeted anatomy that correspond to and
simulate real-time movement of the anatomy.

[0023] In addition, during a medical procedure being performed during the
second time interval, such as navigating an instrument, such as a
catheter or needle to a targeted anatomy, the location(s) of an
electromagnetic coil coupled to the instrumentation during the second
time interval can be superimposed on an image of the instrumentation. The
superimposed image(s) of the instrument can then be superimposed on the
selected image(s) from the first time interval, providing simulated real
time imaging of the instrument location relative to the targeted anatomy.
This process and other related methods are described in pending U.S.
patent application Ser. No. 10/273,598, entitled Methods, Apparatuses,
and Systems Useful in Conducting Image Guided Interventions, filed Nov.
8, 2003, the entire disclosure of which is incorporated herein by
reference.

[0024] Having described above various general principles, several example
embodiments of these concepts are now described. These embodiments are
only examples, and many other embodiments are contemplated by the
principles of the invention, and will be apparent to the artisan in view
of the general principles described above and the exemplary embodiments.
For example, other possible embodiments can be used to perform some or
all of the functions described herein, such as those systems and methods
described in U.S. patent application Ser. No. 11/224,028, filed Sep. 13,
2005, entitled "Apparatus and Method for Image Guided Accuracy
Verification" (referred to herein as "the '028 application"), the
disclosure of which is hereby incorporated by reference in its entirety.

[0025] FIGS. 1 and 2 are schematic illustrations of devices that can be
used to perform various procedures described herein. An apparatus 10
includes two or more markers or fiducials 22 coupled to a dynamic body B
at selected locations, as shown in FIG. 1. The dynamic body B can be, for
example, a selected dynamic portion of the anatomy of a patient. The
markers 22 are constructed of a material that can be viewed on an image,
such as an X-ray. The markers 22 can be, for example, radiopaque, and can
be coupled to the dynamic body B using known methods of coupling such
devices to a patient, such as with adhesive, straps, etc. FIGS. 1 and 2
illustrate the apparatus 10 having four markers 22, but any number of two
or more markers can be used.

[0026] An imaging device 40 can be used to take images of the dynamic body
B while the markers 22 are coupled to the dynamic body B,
pre-procedurally during a first time interval. As stated above, the
markers 22 are visible on the images and can provide an indication of a
position of each of the markers 22 during the first time interval. The
position of the markers 22 at given instants in time through a path of
motion of the dynamic body B can be illustrated with the images. The
imaging device 40 can be, for example, a computed tomography (CT) device
(e.g., respiratory-gated CT device, ECG-gated CT device), a magnetic
resonance imaging (MRI) device (e.g., respiratory-gated MRI device,
ECG-gated MRI device), an X-ray device, or any other suitable medical
imaging device. In one embodiment, the imaging device 40 is a computed
tomography--positron emission tomography device that produces a fused
computed tomography--positron emission tomography image dataset. The
imaging device 40 can be in communication with a processor 30 and send,
transfer, copy and/or provide image data taken during the first time
interval associated with the dynamic body B to the processor 30.

[0027] The processor 30 includes a processor-readable medium storing code
representing instructions to cause the processor 30 to perform a process.
The processor 30 can be, for example, a commercially available personal
computer, or a less complex computing or processing device that is
dedicated to performing one or more specific tasks. For example, the
processor 30 can be a terminal dedicated to providing an interactive
graphical user interface (GUI). The processor 30, according to one or
more embodiments of the invention, can be a commercially available
microprocessor. Alternatively, the processor 30 can be an
application-specific integrated circuit (ASIC) or a combination of ASICs,
which are designed to achieve one or more specific functions, or enable
one or more specific devices or applications. In yet another embodiment,
the processor 30 can be an analog or digital circuit, or a combination of
multiple circuits.

[0028] The processor 30 can include a memory component 32. The memory
component 32 can include one or more types of memory. For example, the
memory component 32 can include a read only memory (ROM) component and a
random access memory (RAM) component. The memory component can also
include other types of memory that are suitable for storing data in a
form retrievable by the processor 30. For example, electronically
programmable read only memory (EPROM), erasable electronically
programmable read only memory (EEPROM), flash memory, as well as other
suitable forms of memory can be included within the memory component. The
processor 30 can also include a variety of other components, such as for
example, co-processors, graphic processors, etc., depending upon the
desired functionality of the code.

[0029] The processor 30 can store data in the memory component 32 or
retrieve data previously stored in the memory component 32. The
components of the processor 30 can communicate with devices external to
the processor 30 by way of an input/output (I/O) component (not shown).
According to one or more embodiments of the invention, the I/O component
can include a variety of suitable communication interfaces. For example,
the I/O component can include, for example, wired connections, such as
standard serial ports, parallel ports, universal serial bus (USB) ports,
S-video ports, local area network (LAN) ports, small computer system
interface (SCSI) ports, and so forth. Additionally, the I/O component can
include, for example, wireless connections, such as infrared ports,
optical ports, Bluetooth® wireless ports, wireless LAN ports, or the
like.

[0030] The processor 30 can be connected to a network, which may be any
form of interconnecting network including an intranet, such as a local or
wide area network, or an extranet, such as the World Wide Web or the
Internet. The network can be physically implemented on a wireless or
wired network, on leased or dedicated lines, including a virtual private
network (VPN).

[0031] As stated above, the processor 30 can receive image data (also
referred to herein as "image dataset") from the imaging device 40. The
processor 30 can identify the position of selected markers 22 within the
image data or voxel space using various segmentation techniques, such as
Hounsfield unit thresholding, convolution, connected component, or other
combinatory image processing and segmentation techniques. The processor
30 can determine a distance and direction between the position of any two
markers 22 during multiple instants in time during the first time
interval, and store the image data, as well as the position and distance
data, within the memory component 32. Multiple images can be produced
providing a visual image at multiple instants in time through the path of
motion of the dynamic body. The processor 30 can also include a receiving
device or localization device 34, which is described in more detail
below.

[0032] As shown in FIG. 2, during a second time interval, two or more
localization elements 24 are coupled to the markers 22 for use during a
medical procedure to be performed during the second time interval. The
localization elements 24 are coupled to the patient adjacent the markers
22. The localization elements 24 can be, for example, electromagnetic
coils, infrared light emitting diodes, and/or optical passive reflective
markers. The markers 22 can include plastic or non-ferrous fixtures or
dovetails or other suitable connectors used to couple the localization
elements 24 to the markers 22. A medical procedure can then be performed
with the markers 22 coupled to the dynamic body B at the same location as
during the first time interval when the pre-procedural images were taken.
During the medical procedure, the localization elements 24 are in
communication or coupled to the localization device 34 included within
processor 30. The localization device 34 can be, for example, an analog
to digital converter that measures voltages induced onto localization
coils in the field; creates a digital voltage reading; and maps that
voltage reading to a metric positional measurement based on a
characterized volume of voltages to millimeters from a fixed field
emitter. Position data associated with the localization elements 24 can
be transmitted or sent to the localization device 34 continuously during
the medical procedure during the second time interval. Thus, the position
of the localization elements 24 can be captured at given instants in time
during the second time interval.

[0033] The image dataset, the position data for the markers from the first
time interval ("model space") and the position data for the localization
elements during the second time interval ("physical space") can be used
to perform an automatic segmentation, correlation and registration
between the data in the model space and the data in the physical space.
The result of the analysis is to provide a physician with images that
represent the position of a dynamic body during the second time interval
when the physician is performing a medical procedure on the dynamic body.
The processor 30 can be configured to perform the automatic
segmentation-correlation-registration process as described in more detail
below.

[0034] To identify actual position data associated with the markers 22
within the image dataset, the processor 30 can perform an automated
segmentation procedure. Segmentation is the process of identifying
reference points in the 3-D image dataset. The purpose of the
segmentation is to automatically locate potential "landmarks" in the
dataset that indicate a location where a marker 22 may be located.
Segmentation can be performed in a variety of different manners. For
example, a segmentation process can include, intensity filtering,
connectivity analysis, and size and shape filtering to identify candidate
sensor (e.g., marker) locations, or model space (also referred to herein
as "image space") coordinates of the marker 20 candidates. In some
example embodiments, the intensity filtering applies domain knowledge to
threshold the 3-D image dataset to select only those image values that
fall within a designated intensity range that contains the reference
points. For example, reference markers can be designated to appear in CT
scans with Hounsfield units higher than the anatomical structures within
the 3-D image. An example output from an intensity filtering process can
include a 3-D binary volume with non-zero entries indicating voxels
(i.e., a 3-D data point) with an intensity that falls within the range of
values that characterize an image marker, as illustrated in FIG. 8. FIG.
8 is a schematic illustration of the flow of information during one
example of an automatic segmentation process.

[0035] After filtering the image values based on intensity, a connectivity
analysis can be performed. A connectivity analysis can use the output
from the intensity filtering to analyze the potential candidates
identified in the 3-D image dataset to identify, for example,
"connected-components." A connected-component, as used here, is a
continuous 3-D region in the 3-D volume (i.e., image dataset) that is
connected via adjacent voxels that are in contact with one another.
Examples of voxels of connected-components are illustrated in FIGS. 5 and
6. FIG. 5 illustrates a connected-component having 8 connected voxel
elements (indicated by the shaded boxes), and FIG. 6 illustrates a
connected component having 4 connected voxel elements (indicated by the
shaded boxes). From the identified connected-components, information
about the connected regions, such as the location of each voxel element,
the geometric center, and the volume and bounding perimeter dimensions
can be identified. An example output of a connectivity analysis can
include, for example, a list of each separate connected-component in the
3-D binary volume, and can be used in the next step in the segmentation
process.

[0036] Next, in some embodiments, the output from the connectivity
analysis, which includes the identified connected-components, can be
filtered based on size and shape criteria during a size threshold
analysis. First, knowledge about the size of the reference markers can be
used to identify and discard any connected-components that are too small
or too large to be valid markers. A list of connected-components that
fulfill the size criteria can then be evaluated based on the shape of the
components during a shape-filtering analysis. Knowledge about the shape
of the reference markers can be used to discard any components that do
not match the known shape of the reference markers. For example, if the
markers are known to be cylindrical, then the connected component shape
can be analyzed to determine if the ratio between the major axis and the
minor axis is within a set criteria. The output from this step in this
example process includes, for example, a list of connected-components
that fulfill the shape criteria. Other analysis can be performed
depending on the particular marker configuration, such as, for example,
checking whether the connected-component shape is symmetric about a
centroid of the connected-component.

[0037] After the segmentation process is performed, an automatic
correlation process can be performed. Correlation as used here is the
process of correctly matching reference points between the image or model
space and the physical space. Correctly matching the reference points
aids in accurately computing the registration between the data in the
image space and the data in the physical space without user interaction.
The correlation process determines where each of the localization
elements is positioned in the model images. Correct correlation is
required to compute an affine transform between model space and physical
space. The apparatuses and methods described herein enable the process to
be automated with minimal user intervention. Automatic correlation
results in an understanding of the location of the markers in image space
and physical space, as well as the corresponding labeling/identification
of each marker in each space.

[0038] Because there are a large number of possible solutions,
computations of all possible combinations can result in long computation
times. According to an embodiment of the invention, the processor 30 can
be configured to compute the correlation between the image space and the
physical space at a much faster rate (e.g., 2 seconds on a 1.5 GHz G4
Macintosh computer).

[0039] Because the number of localization element positions in the
physical space is typically smaller than the number of identified marker
positions in the model space, a guess at a correlation can be made for
three localization element points in physical space. An affine transform
registration is then computed between the selected positions of the
localization elements 24 in physical space and the model space. The
computed registration is then used to transform the remaining
localization element positions to model space and determine if any
markers exist at the projected locations. A brute force iteration is made
in groups of 3 as just described. When projecting the remaining points
from physical space to model space to test the correlation guess, a test
can be performed for the existence of a marker in model space within a
settable threshold 3-D distance. If present, a 3-D error can be computed
and the correlation resulting in the lowest error can be noted and
recorded. This technique discards points in model space that do not have
a corresponding point in physical space (i.e., false positives in the
list of marker positions determined during segmentation).

[0040] Because the number of localization element positions is relatively
low, it can be fairly computationally inexpensive to perform the
iterative process described above to search all possible correlation
combinations. The process is implemented such that the affine transform
used to compute rigid body registration between the model space and the
physical space for each 3-point correlation is abstract, and the actual
implementation can be defined and determined at runtime. It is possible
to improve the speed of the process by stopping the solution search
iterations if a solution is identified that meets the specified criteria.
For example, when computing the error for a correlation guess, the
projection loop-and-fail for the correlation guess can be reduced if any
single reference point in the physical space fails to map to a point in
model space within a specified error threshold. Each potential
correlation combination is evaluated by one or more criteria to determine
the correlation between segmented markers in model space and physical
localization element locations. Examples of evaluation criteria include
computing the transformation using three points, and then projecting the
remaining physical points to model space as described previously. Other
examples include incorporating coil orientation information between the
segmented markers and 5- or 6-degrees of freedom (DOF) localization
elements, or applying externally available information, such as requiring
the user to attach the localization elements in a certain configuration.
This correlation technique can account for physical localization elements
being in a slightly different relative position than the model space
markers since the localization elements process can be performed when the
localization elements are anywhere in the periodic cycle of the dynamic
body.

[0041] After the correlation process, the processor 30 can perform an
automatic registration process. The process of registration tracks
temporal movement of the dynamic body via the movement of the markers 22,
and when temporally valid, computes the transformation between the
physical space and the model space.

[0042] A measure of a temporal position is referred to herein as a
"cost-function." An automatic registration algorithm uses abstract
objects so that the cost-function can be defined/determined at runtime.
For example, one possible cost function is the average distance between
reference points (e.g., positions of localization elements 24).
Cost-functions can compute a temporal measure for a group of reference
points independent of the space in which the points are known since the
measure is based upon landmark positions relative to each other. Once the
correlation is established, the localization element locations in
physical space can be periodically evaluated using a cost-function to
determine when the dynamic body most closely matches the point in the
periodic phase of the first time interval (image acquisition). Examples
of cost-functions can include: average distance between markers; max/min
axis ratio of bounding ellipsoid; and a ratio between minimum and maximum
3D distance between markers. The cost-function can be, for example,
determined in patient model space to ensure that moving the patient
and/or localizing machinery will not affect the
outcome/solution/computation.

[0043] A cost-function can be used to establish a measure of the marker
positions within the plurality of images during the first time interval.
The same cost-function can then be applied continuously to the correlated
localization element positions during the second time interval. When the
cost-function indicates that the positions of the localization elements
in the second time interval have the same relative positions as the
marker positions in the first time interval, then the dynamic body can be
identified as being at the same temporal point along the path of motion
as the first time interval. During the time that the cost-function
indicates that the dynamic body is at the same temporal point along the
path of motion as the first time interval, then the automatically
correlated markers from the first time interval and localization elements
from the second time interval can be used to automatically compute a
registration. When the cost-function indicates that the registration is
valid, then the position and navigational path of a medical instrument
can be displayed on a computer screen superimposed onto images of the
dynamic body acquired during the first time interval.

[0044] After performing the automated segmentation and correlation
processes, a list of position data for the localization elements 24 in
image space is obtained. This represents the position of the markers 22,
and therefore the position of the dynamic body B when the image dataset
was acquired. This information is used as the "temporal reference" for
the image dataset and represents the nominal reference point position for
the dataset. For multiple images acquired at different points in the
patient temporal cycle (e.g., at inspiration and expiration of the
respiratory cycle), the segmentation-correlation process can be repeated
and a temporal reference position can be determined for each image.

[0045] Once the temporal reference is established for each image dataset,
a registration filter can be used to compare the position of the
localization elements 24 in the physical space to the temporal reference
location for each image dataset. If the positions of the localization
elements 24 are sufficiently close to the temporal reference for a
dataset (i.e., the image dataset), then the dataset can be used for
navigation for that temporal moment by computing the affine
transformation between the physical space and model space. The
transformation is then used to project information such as reformatted
images, segmentations, informatics, etc. The threshold that determines
how close the physical configuration must be to the locations in the
image dataset can be modified at runtime to allow the sensitivity or
temporal resolution to be modified.

[0046] Through the automatic registration process, the relative marker
positions at the time of the 3-D scan can be determined. This acquisition
of relative marker position allows the point in the respiratory cycle at
which the scan was acquired to be determined and navigation gated to that
same point in the cycle during a subsequent medical procedure. The
resulting registration is relative to the markers affixed to the patient,
which allows the patient to be repositioned relative to the scan gantry,
table position, and/or localization machinery without invalidating the
registration, as long as the markers remain in a fixed position relative
to the patient.

[0047] As stated previously, the automatic
segmentation-correlation-registration process can be performed using an
apparatus that includes a garment, such as a garment disclosed in the
'028 application. Such an apparatus can be used with the systems and
methods described herein to perform the same
automatic-segmentation-registration processes described above, except in
such an embodiment, the markers and localization elements are coupled to
the patient through the use of a garment. All other devices described
with reference to FIGS. 1 and 2 can be used in this embodiment to perform
the same automatic segmentation-correlation-registration processes as
described above.

[0048]FIG. 3 illustrates an apparatus 210 that includes a garment 220
that is tubular shaped and can be constructed with a flexible and/or
stretchable material. This particular garment configuration is only one
example of a garment that can be used. It should be understood that other
garment configurations can alternatively be used, such as those described
in the '028 application. The apparatus 210 further includes multiple
markers or fiducials 222 coupled to the garment 220 at spaced locations.
A plurality of localization elements 224 are removably coupled proximate
to the locations of markers 222, such that during a first time interval
as described above, images can be taken without the elements 224 being
coupled to the garment 220. In other embodiments, the localization
elements 224 need not be removably coupled to the markers 222. For
example, the localization elements 224 can be fixedly coupled to the
garment 220. In addition, the localization elements 224 can be coupled to
the garment 220 during the pre-procedure imaging.

[0049] The garment 220 can be positioned over a portion of a patient's
body (proximate dynamic anatomy), such as around the upper or lower torso
of the patient at a fixed location relative to the patient during both a
first time period, in which images are taken of the dynamic anatomy
(model or image space), and during a second time period, in which a
medical procedure is being performed on the dynamic anatomy (physical
space). The stretchability of the garment 220 allows the garment 220 to
at least partially constrict some of the movement of the portion of the
body for which it is coupled. The markers 222 are coupled to the garment
220 at a fixed location on the garment 220, thus the markers 222 are also
coupled to the patient at a fixed location relative to the dynamic
anatomy during both the first time period and the second time period.

[0050] FIG. 4 is a graphical illustration indicating how the apparatus 210
(shown without localization elements 224) can move and change orientation
and shape during movement of a dynamic body, such as a mammalian body M.
The graph is one example of how the lung volume can change during
inhalation (inspiration) and exhalation (expiration) of the mammalian
body M. The corresponding changes in shape and orientation of the
apparatus 210 during inhalation and exhalation are also illustrated.
Although FIG. 4 is being described with reference to an embodiment
including a garment, an embodiment that does not include a garment can be
similarly described. The six markers 222 shown in FIG. 3 are labeled a,
b, c, d, e, and f. As described above, images of the dynamic anatomy with
the apparatus 210 coupled thereto can be taken during a first time
interval. The images can include an indication of relative position of
each of the markers 222, that is the markers 222 are visible in the
images, and the position of each marker 222 can then be identified over a
period of time. As illustrated, during expiration of the mammalian body M
at times indicated as A and C, a distance X between markers a and b is
smaller than during inspiration of the mammalian body M, at the time
indicated as B. Likewise, a distance Y between markers b and f is greater
during inspiration than during expiration.

[0051] FIG. 7 is a flowchart illustrating a method according to another
embodiment of the invention. A method includes at step 80 receiving
during a first time interval image data associated with a plurality of
images of a dynamic body. The plurality of images include an indication
of a position of a first marker on a garment coupled to the dynamic body
and a position of a second marker on the garment coupled to the dynamic
body. The first marker is coupled to the garment at a first location and
the second marker is coupled to the garment at a second location. The
first time interval is associated with a path of motion of the dynamic
body. At step 82, data associated with a position of a first localization
element relative to the dynamic body is received, and data associated
with a position of a second localization element relative to the dynamic
body is received during a second time interval after the first time
interval. The first localization element is coupled to the garment at the
first location, and the second localization element is coupled to the
garment at the second location. The second time interval is associated
with a path of motion of the dynamic body and the garment is coupled to
the dynamic body in a fixed position relative to the dynamic body during
both the first time interval and the second time interval.

[0052] During the second time interval, an image from the plurality of
images associated with a position of the first marker that is
substantially the same as the position of the first localization element
relative to the dynamic body and a position of the second marker that is
substantially the same as the position of the second localization element
relative to the dynamic body are automatically identified at step 84. The
automatic identification can be based on an appearance of the markers
within the identified image. The automatic identification can also
include identifying a position of a third localization element, and
projecting that position on to the image data set and determining whether
a third marker exists in an image from the image data set at the position
of the third localization element. The automatic identification can also
include correlating a position of the first localization element during
the second time interval with the position of the first marker in the
plurality of images. At step 86, the path of motion of the dynamic body
is automatically registered during the second time interval is
automatically registered with the path of motion of the dynamic body
during the first time interval. The automatic registering in step 86 can
include identifying at least one temporal reference within the plurality
of images and identifying whether the at least one temporal reference is
associated with at least one of the first marker or the second marker
providing a navigational path for a medical instrument to be directed
based on the identified image.

[0053] At step 88, a navigational path is provided for a medical
instrument to be directed based on the identified image. A physician can
use the navigational path to guide a medical instrument to the dynamic
body while performing a medical procedure on the dynamic body during the
second time interval.

CONCLUSION

[0054] While various embodiments of the invention have been described
above, it should be understood that they have been presented by way of
example only, and not limitation. Thus, the breadth and scope of the
invention should not be limited by any of the above-described
embodiments, but should be defined only in accordance with the following
claims and their equivalents.

[0055] The previous description of the embodiments is provided to enable
any person skilled in the art to make or use the invention. While the
invention has been particularly shown and described with reference to
embodiments thereof, it will be understood by those skilled in art that
various changes in form and details may be made therein without departing
from the spirit and scope of the invention. For example, the garment,
markers and localization elements can be constructed from any suitable
material, and can be a variety of different shapes and sizes, not
necessarily specifically illustrated, while still remaining within the
scope of the invention.

[0056] While a relatively small number of markers are discussed, the
system is scalable and the use of any number of markers is contemplated.
For example, a garment may include between 2 and 20 markers, 10-50
markers, etc. Additionally, variations in the automated processes can be
used to achieve the same, or substantially the same, desired results.

Patent applications by Evan Austill, Jr., Nashville, TN US

Patent applications by Jerome R. Edwards, Nashville, TN US

Patent applications by Torsten M. Lyon, Golden, CO US

Patent applications by Troy L. Holsing, Nashville, TN US

Patent applications in class With means for determining position of a device placed within a body

Patent applications in all subclasses With means for determining position of a device placed within a body